A dynamic social norm messaging intervention to reduce meat consumption: A randomized cross-over trial in retail store restaurants. (1st February 2022)

• Record Type: Journal Article
• Authors: Çoker, Elif Naz; Pechey, Rachel; Frie, Kerstin; Jebb, Susan A.; Stewart, Cristina; Higgs, Suzanne; Cook, Brian
• Is Part Of: Appetite. Volume 169(2022)
• Publication Date: 2022-02-01
• Subjects: Meat consumption -- Social norms -- Dynamic norms -- Intervention -- Social influences -- Dietary choice; Food habits -- Periodicals; Appetite -- Periodicals; Appetite disorders -- Periodicals; Electronic journals; 306.4613
• DOI: 10.1016/j.appet.2021.105824
• Languages: English
• ISSNs: 0195-6663

Abstract

Abstract: Perceptions of social norms around eating behavior can influence food choices. Communicating information about how others are changing their eating behavior over time (dynamic descriptive social norms) may motivate individuals to change their own food selection and consumption. Following a four-week baseline period, 22 in-store restaurants of a major retail chain across the UK were randomized to display a dynamic descriptive social norm message intended to motivate a shift from meat-to plant-based meals either during the first two, or last two weeks of the four-week study period. A linear regression model showed there was no evidence of an effect of the intervention (β = -0.022, p = .978, 95% CIs: −1.63, 1.58) on the percentage sales of meat- vs plant-based dishes. Fidelity checks indicated that adherence to the intervention procedure was often low, with inconsistencies in the placement and display of the intervention message. In four stores with high fidelity the estimated impact of the intervention was not materially different. The lack of apparent effectiveness of the intervention may reflect poor efficacy of the intervention or limitations in its implementation in a complex food purchasing environment. The challenges highlighted by this study should be considered in future design and evaluation of field trials in real-world settings.